Sains Malaysiana 48(11)(2019): 2575–2581
http://dx.doi.org/10.17576/jsm-2019-4811-27
The Indian Mackerel Aggregation Areas in
Relation to Their Oceanographic Conditions
(Perkaitan Kawasan Pengumpulan Ikan Kembung
India dan Keadaan Oseanografi)
YENY NADIRA,
K.1,2,
MUSTAPHA,
M.A.1,3*
& GHAFFAR, M.A.2
1Centre for Earth
Sciences and Environment, Faculty of Science and Technology, Universiti
Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
2School of Fisheries
and Aquaculture Sciences, Universiti Malaysia Terengganu, 21300
Kuala Terengganu, Terengganu Darul Iman, Malaysia
3Institute of Climate
Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor
Darul Ehsan, Malaysia
Received: 15 April 2019/Accepted:
15 August 2019
ABSTRACT
In order to determine the favourable
oceanographic conditions which influence fish aggregation areas,
the integration of remote sensing and GIS technique was applied. This
paper aims to classify the spatial distribution and abundance of
R.
kanagurta in the South China Seas (SCS) using principal component
analysis (PCA) and cluster analysis (CA).
Remotely-sensed satellite oceanographic data of chlorophyll-a concentration
(chl-a), sea surface temperature (SST) and sea surface height (SSH)
together with high catch fish data were used to characterize seasonal
abundance of the R. kanagurta. PCA identified two principal components
that had eigenvalues >1 (PC1 and PC2)
which accounted for 59.3% of the cumulative variance. Factor loading
in the PCA
proved that all environmental variables used in this
study; chl-a (PC1),
SSH
and SST (PC2)
had influenced the CPUE of R. kanagurta. Using CA,
two clusters of CPUE abundance were identified. In cluster
1, an average CPUE of 350.7 kg/m³ with highest catch
were recorded in January, April, May, July and October. Meanwhile,
in cluster 2, an average CPUE of 1033.9 kg/m³ with highest catch were recorded in
April, May, September and October. Preferred range for fish aggregations
showed SST,
SSH
and chl-a were observed in between 29-31°C,
1.12-1.28 m and 0.24-0.42 mg/m3, respectively. Binary habitat suitability
index was used to model the potential aggregation areas. The highest
potential fish aggregations areas of R. kanagurta were found
located along the coast of Peninsular Malaysia in early and late
Southwest monsoon (at accuracy of 83.68% with kappa of 0.7).
Keywords: Chlorophyll-a;
fish aggregation areas; Rastrelliger kanagurta; sea surface
height; sea surface temperature
ABSTRAK
Integrasi antara data penderiaan
jauh dan teknik GIS diaplikasi bagi menentukan keadaan
oseanografi yang mempengaruhi kawasan pengumpulan ikan. Objektif
dalam kajian ini adalah untuk mengkelaskan taburan reruang dan kelimpahan
R.
kanagurta di Laut China Selatan menggunakan analisis komponen
prinsipal (PCA)
dan analisis kelompok (CA) serta mengenal pasti perhubungan
antara taburan ikan dengan keadaan persekitaran. Hubungan antara
data taburan klorofil-a (chl-a), suhu permukaan laut
(SST)
dan ketinggian permukaan laut (SSH) daripada satelit penderiaan
jauh serta taburan tangkapan R. kanagurta digunakan untuk
mengenal pasti hubungan taburan musiman ikan pelagik. PCA mengenal pasti dua komponen
prinsipal yang mempunyai nilai eigen >1 (PC1
dan PC2) dengan nilai peratus kumulatif varians adalah 59.3%.
Faktor penentuan dalam komponen prinsipal menunjukkan bahawa parameter
persekitaran mempengaruhi data tangkapan ikan. CA menunjukkan
dua kelompok tangkapan ikan dengan kelompok 1, nilai purata tangkapan
ikan sebanyak 350.7 kg/m³ dengan catatan tangkapan ikan tertinggi
pada bulan Januari, April, Mei, Julai, September dan Oktober. Manakala,
di dalam kelompok 2, nilai purata tangkapan ikan sebanyak 1033.9
kg/m³ dengan catatan tangkapan ikan tertinggi pada bulan
April, Mei, September dan Oktober. Julat kesesusaian cerapan pengumpulan
ikan bagi SST,
SSH
dan chl-a didapati pada suhu 29-31°C, 1.12-1.28
m dan 0.24-0.42 mg/m³. Kawasan berpotensi bagi pengumpulan R.
kanagurta yang dimodel menggunakan indeks kesesuaian habitat
mendapati kawasan pengumpulan R. kanagurta paling berpotensi
terletak di sepanjang perairan pantai Semenanjung Malaysia pada
permulaan dan akhir musim monsun barat daya (pada ketepatan 83.68%
dengan nilai kappa 0.7).
Kata kunci: Kawasan pengumpulan ikan; ketinggian permukaan laut;
klorofil-a; Rastrelliger kanagurta; suhu permukaan laut
REFERENCES
Akhir, M.F. 2014. Review of current circulation studies in the Southern
South Shina Sea. Journal of Sustainability Science and Management
9(2): 21-30.
Akhir, M.F., Daryabor, F., Husain, M.L., Tangang, F. & Qiao,
F. 2015. Evidence of upwelling along Peninsular Malaysia during
Southwest monsoon. Open Journal of Marine Science 5: 273-279.
Andrade, H.A. & Garcia, C.A.E. 1999. Skipjack tuna fishery in
relation to sea surface temperature off the southern Brazillian
coast. Fisheries Oceanography 8(4): 245-254.
Anon. 1976. Plankton, fish eggs and larvae studies. UNDP/FAO Pelagic
Fishery Project (IND/69/593). Progress Report No. 17. p. 27.
Bradley, P. & Fayyad, U. 1998. Refining initial points for k-means
clustering. Proc. 15th International Conf. on Machine Learning.
Chandran, R.V., Jeyaram, A., Jayaraman, V., Manoj, S., Rajitha, K.
& Mukherjee, C.K. 2009. Prioritization of satellite derived
potential fishery grounds: An analytical hierarchical approach-based
model using spatial and non-spatial data. International Journal
of Remote Sensing 30(17): 4479-4491.
Chassot, E., Bonhommeau, S., Reygondeau, G., Nieto, K., Polovina,
J.J., Huret, M., Dulvy, N.K. & Demarcq, H. 2011. Satellite remote
sensing for an ecosystem approach to fisheries management. ICES
Journal of Marine Science 68: 651-666.
Collette, B.B. & Russo, J.L. 1984. Morphology, systematics and
biology of the Spanish mackerels (Scomberomous, Scombridae).
Fish Bulletin US 82: 545-692.
Fang, W. & Fang, G. 1998. The recent progress in the study of
the Southern South China Sea circulation. Advance in Earth Sciences
13(2): 166-172.
Frontier, S. 1976. Étude de la decroissance des valeurs propers dans
une analyze en composantes principals comparison avec le modèle
de baton brisé. J. Exp. Mar. Biol. Ecol. 25: 67-75.
Gambang, A.C., Ramli, H.B. & Awang, D. 2003. Overview of biology
and exploitation of the small pelagic fish resources of the EEZ
of Sarawak, Malaysia. National Fisheries Symposium, Kota
Bharu, Kelantan.
Grim, J., Novovicova, J., Pudil, P., Somol, P. & Ferri, F. 1998.
Initialization normal mixtures of densities. Proc. Int’l Conf.
Pattern Recognition (ICPR 1998).
Guttman, L. 1954. A new approach to factor analysis: The radex. In
Mathematical Thinking in the Social Sciences, edited by Lazarsfeld,
P.F. Glencoe: The Free Press.
Jackson, D.A. 1993. Stopping rules in principal components analysis:
A comparison of heuristical and statistical approaches. Ecology
74: 2204-2214.
Jolliffe, I.T. 1986. Principal Component Analysis. New York:
Springer-Verlag.
Lanz, E., Manuel, N.M., Juana, L.M. & Dworak, J.A. 2009. Small
pelagic fish catches in the Gulf of California associated with sea
surface temperature and chlorophyll. CalCOFI Report 50: 134-146.
Laevastu, T. & Hayes, M.L. 1981. Fisheries Oceanography and
Ecology. Oxford: Fishing News.
Legendre, P. & Legendre, L.F. 1998. Paper presented at the Numerical
Ecology, Amsterdam, Netherlands.
Mansor, M. 1989. Tumbesaran, kematian dan corak pengrekrutan Ikan
Kembung Rastrelliger kanagurta (Cuvier) di Pantai Barat Semenanjung
Malaysia. Fisheries Bulletin 59 (Jabatan Perikanan: Kementerian
Pertanian Malaysia). p. 22.
Mansor, M., Abdullah, S. & Hamid, A. 1996. Population structure
of small pelagic fisheries off the East Coast of Peninsular Malaysia.
Fisheries Bulletin 99 (Jabatan Perikanan: Kementerian Pertanian
Malaysia), 30.
MMD. 2016. Malaysian Meteorological Department. http://www. met.gov.my/web/metmalaysia/climate/generalinformation/
malaysia.
Mohsin, K.M. & Mohamed, M.I. 1988. Ekspedisi Matahari’ 87: A
Study on the Offshore Waters of the Malaysian EEZ. Occasional
Publication No. 8. Universiti Pertanian Malaysia.
Moore, A. 1998. Very fast em-based mixture model clustering using
multiresolution kd-trees. Proc. Neural Info. Processing Systems
(NIPS 1998).
Nishida, T., Miyashita, K. & Lyne, H. 1999. Spatial dynamics
of sothern bluefin tuna recruitment. Proceeding of the First
International Symposium on GIS in Fishery Sciences, Seattle.
Nurdin, S., Mustapha, M.A., Lihan, T. & Tangang, F. 2017a. Spatial
and temporal variability of the chlorophyll-a concentration
in Makassar Strait using Empirical Orthogonal Function analysis
of satellite images. Indian Journal of Geo Marine Sciences 46(07):
1381-1389.
Nurdin, S., Mustapha, M.A., Lihan, T. & Zainuddin, M. 2017b.
Applicability of remote sensing oceanographic data in the detection
of potential fishing grounds of Rastrelliger kanagurta in
the archipelagic waters of Spermonde, Indonesia. Fisheries Research
196: 1-12.
Nurdin,
S., Mustapha, M.A. & Lihan, T. 2014. The relationship between
sea surface temperature and chlorophyll-a concentration in
fisheries aggregation area in the archipelagic waters of spermonde
using satellite images. The 2013 UKM FST Postgraduate Colloquium,
UKM.
Pena,
H., Gonzalez, C. & Vejar, F. 1999. Study of spatial dynamics
of Jack Mackerel fishing grounds and environmental conditions using
GIS. Proceeding of The First International Symposium on GIS in
Fishery Sciences, Seattle.
Polovina,
J.J. & Howell, E.A. 2005. Ecosystem indicators derived from
satellite remotely sensed oceanographic data for the North Pacific.
ICES Journal of Marine Science 62: 31-27.
Rebert,
J.P., Donguy, J.R., Elidin, G. & Wyrtki, K. 1985. Relations
between sea level, thermocline depth, heat content and dynamic height
in the tropical Pacific Ocean. Journal Geophysical Research 90:
C611719-C611725.
Shaari,
N.R. & Mustapha, M.A. 2018. Predicting potential Rastrelliger
kanagurta fish habitat using MODIS data and GIS Modelling: A
case study of Exclusive Economic Zone, Malaysia. Sains Malaysiana
47(7): 1369-1378.
Shaari,
F. & Mustapha, M.A. 2017. Factors influencing the distribution
of Chl-a along Coastal Waters of East Peninsular Malaysia.
Sains Malaysiana 46(8): 1191-1200.
Skjoldal,
H.R. 2004. Fish stocks and fisheries in relation to climate variability
and exploitation natural resource system challenge: Ocean and aquatic
ecosystem. In Encyclopedia of Life Supporting System (EOLSS),
developed under the auspices of the UNESCO, Oxford, UK.
Solanki,
H.U., Dwivedi, R.M., Nayak, S.R., Naik, S.K., John, M.E. & Somvanshi,
V.S. 2005. Cover: Application of remotely sensed closely coupled
biological and physical process for marine fishery resources exploration.
International Journal of Remote Sensing 26(10): 2029-2034.
Solanki,
H.U., Dwivedi, R.M. & Nayak, S.R. 2001. Synergistic analysis
of SeaWiFS chlorophyll concentration and NOAA-AVHRR SST features
for exploring marine living resources. International Journal
of Remote Sensing 22: 3877-3882.
Suhaila,
J. & Jemain, A.A. 2009. A comparison of the rainfall patterns
between stations on the East and the West Coasts of Peninsular Malaysia
using the smoothing model of rainfall amounts. Meteorol. Appl.
16(3): 391-401.
Susanto,
R.D., Moore, T.S. & Marra, J. 2006. Ocean color variability
in the Indonesian seas during the SeaWiFS era. Geochem. Geophy.
Geosy. 7(5): 1-16.
Tang,
D.L. & Kawamura, H. 2002. Ocean color monitoring of coastal
environments in the Asian waters. Journal Korea Society Oceanography
37: 154-159.
Vasconcelos, R.P., Le Pape, O., Costa, M.J. & Cabral, H.N. 2013.
Predicting estuarine use patterns of juvenile fish with Generalized
Linear Models. Estuar. Coast.
Shelf Sci. 120: 64-74. http://dx.doi.org/10.1016/j.ecss.2013.01.018.
Wilson,
C. 2011. The rocky road from research to operations for satellite
ocean-color data in fishery management. ICES Journal of Marine
Science 68(4): 677-686.
Wyrtki,
K. 1961. Physical oceanography of the Southeast Asian waters. NAGA
Report Vol. 2. Scientific Results of Marine Investigations
of the South China Sea and the Gulf of Thailand 1959-1961. The
University of California, Scripps Institution of Oceanography, La
Jolla, California. pp. 1-195.
Xie,
S.P., Xie, Q., Wang, D. & Liu, W.T. 2003. Summer upwelling in
the South China Sea and its role in regional climate variations.
Journal of Geophysical Research 108(C8): 3261.
Xian,
T., Sun, L., Yang, Y. & Fu, Y. 2012. Monsoon and eddy forcing
of Chlorophyll-a variation in the Northeast South China Sea.
International Journal of Remote Sensing 33(23): 7431-7443.
Yahaya,
N.A.Z., Musa, T.A., Omar, K.M., Din, A.H.M., Omar, A.H., Tugi, A.
& Wahab, M.I.A. 2016. Mean Sea Surface (MSS) Model determination
for Malaysian Seas using Multi- Mission Satellite Altimeter. The
International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences. Volume XLII-4/W1. International
Conference on Geomatic and Geospatial Technology (GGT). pp.
3-5.
Yanagi,
T., Sachoemar, S.I., Takao, T. & Fujiwara, S. 2001. Seasonal
variation of stratification in the Gulf of Thailand. Journal
of Oceanography 57: 461-470.
Yusop,
S.M. & Mustapha, M.A. 2019. Influence of oceanographic parameters
on the seasonal potential fishing grounds of Rastrelliger kanagurta
using maximum entropy models and remotely sensed data. Sains
Malaysiana 48(2): 259-269.
Zagaglia,
C.Z., Lorenzzetti, J.A. & Stech, J.L. 2004. Remote sensing data
and longline catches of yellowfin tuna (Thunnus albacares)
in the equatorial Atlantic. Remote Sensing of Environment 93(1-2):
267-281.
Zainuddin,
M. 2007. Mapping of potential fishing grounds of Rastrelliger
kanagurta in Bantaeng waters, South Sulawesi. Jurnal Sains
dan Teknologi 7(2): 57-64.
Zainuddin,
M., Saitoh, K. & Saitoh, S.I. 2008. Albacore (Thunnus alalunga)
fishing ground in relation to oceanographic conditions in the western
North Pacific Ocean using remotely sensed satellite data. Fisheries
Oceanography 17(2): 61-73.
Zheng,
X., Pierce, G.J. & Reid, D.G. 2001. Spatial patterns of Whiting
abundance in Scottish waters and relationship with environmental
variables. Fisheries Research 50: 259-270.
*Corresponding author; email: muzz@ukm.edu.my
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